5 Unpack AI Assumptions and Unknowns from Text

• High-quality and relevant data is crucial for AI's effectiveness because it drives insights, improves algorithms, and enhances overall performance.

Data Dependence

• Structured and unstructured data are essential for AI algorithm training and accurate predictions.

Success Story

• Amazon utilizes vast data for personalized recommendations, which exemplifies a successful data-driven AI application.

Failure Example

• IBM Watson experienced setbacks due to biased and limited datasets, highlighting pitfalls of poor data quality.

Key Considerations

• Data should be diverse, relevant, and free from biases to ensure fair and accurate AI outcomes.

Virtuous Cycle

• Better data leads to better algorithms, which leads to improved service and a continuous cycle of improvement.

Caveats

• Assuming more data ensures better results is not always true and biased data leads to flawed AI.
• The importance of ongoing data validation and updates must be highlighted, along with challenges in obtaining high-quality data.
• Understanding data's role in AI development ensures accurate, relevant, and impactful solutions.

Foundation of AI

• Data serves as the backbone for all AI training and effective decision-making processes.

Data Challenges

• The performance of AI systems is impacted by issues like bias, volume, and the overall quality of data.

Use Cases

• Amazon's recommendation engine exemplifies effective data use in AI applications.

Optimization

• Leverage data to enhance algorithms and improve user experiences.

Continuous Improvement

• Data should be treated as a evolving resource that is continuously refined.

Caveats

• The complexity of data in AI systems should not be oversimplified and ethical data usage and privacy must be considered.
• There is skepticism about the scalability of data-driven AI, therefore, improving data quality should be iterative.
• AI solutions depend on structured, diverse, and high-quality data to deliver meaningful and reliable results.

Data Variety

• Structured, semi-structured, and unstructured data should be included for comprehensive AI training.

Success and Failure Stories

• Contrast Amazon's data success with IBM Watson's data challenges.

Bias Mitigation

• Ensure datasets are diverse and representative to avoid skewed outcomes.

Actionable Insights

• Data should be used to drive decisions and refine strategies.

Scalability

• Build systems capable of handling growing data volumes effectively.

Caveats

• Not all data is equally valuable for AI, biased datasets can undermine AI result credibility, and need exists to balance data quantity with quality.
• You must avoid assuming that the virtuous cycle applies uniformly to all AI use cases.
• Uniform user trust must be maintained in data usage.
• Data monetization transforms AI insights into revenue streams via direct sales, improved personalization, or new products.

Direct Monetization

• Sell anonymized insights, such as healthcare organizations sharing public health research data.

Indirect Monetization

• Personalize content or improve training programs, such as Coursera's tailored recommendations.

Data-Driven Products

• Create AI-powered tools, such as IBM Watson's disaster response models.

Value Expansion

• Use data to create products that benefit partners and customers alike.

Strategic Revenue Streams

• Turn raw data into actionable and profitable solutions.

Caveats

• Ethical pitfalls must be avoided in data sharing with compliance clarified when monetizing sensitive data, and challenges addressed in creating anonymized datasets.
• Critical data decisions, from freshness to compliance, directly affect AI product delivery and customer outcomes.

Data Coverage

• Ensure datasets address user needs comprehensively.

Training Decisions

• Decide how to prepare and select data for model training.

Bias & Ethics

• Identify and mitigate bias in datasets to ensure fairness.

Model Monitoring

• Continuously evaluate model performance and accuracy.

Compliance Focus

• Align data use with privacy and regulatory requirements.

Caveats

• One-size-fits-all data solutions must be avoided and ethical data usage is non-negotiable, challenges need to be addressed when integrating diverse data sources, and scalability and operational implications of data decisions must be highlighted.
• Product managers must oversee data freshness, training decisions, and ethics while ensuring scalability and compliance.

Freshness Matters

• Outdated data undermines AI performance and relevance.

Training Precision

• Ensure data quality for effective model learning.

Bias Awareness

• Monitor and reduce bias in datasets to maintain fairness.

Scalability Needs

• Build systems that grow with data demands.

Compliance & Security

• Protect customer data to avoid legal and ethical risks.

Caveats

• Product managers don't need to be data engineers, clarification needed for cross-functional collaboration, potential blind spots in bias detection must be addressed, and importance highlighted for staying informed on evolving standards.
• Maintaining relevant and comprehensive data ensures AI solutions effectively address user problems and adapt to evolving needs.

Problem Relevance

• Verify that data aligns with customer challenges.

Impact of Outdated Data

• Identify and address gaps that reduce AI accuracy.

Data Source Expansion

• Explore new sources to enhance data coverage.

Licensing Needs

• Secure proper licenses to use external data responsibly.

Iterative Updates

• Regularly refresh data to maintain accuracy and value.

Caveats

• Avoid relying on a single data source without validation, clarify that data freshness requires continuous monitoring, address challenges in identifying new, high-quality data sources, and highlight the risk of using outdated or incomplete data.
• Comprehensive, clean, and trustworthy data underpins AI success by ensuring models learn effectively and deliver value.

Volume Matters

• Ensure enough data to train robust models.

Diversity in Data

• Capture a wide range of perspectives and scenarios.

Velocity

• Monitor how quickly data is updated and processed.

Trust in Data

• Validate the accuracy and reliability of sources.

Ethical Use

• Adhere to responsible practices for data collection and application.

Caveats

• All large datasets aren't valuable, data trustworthiness is more important than sheer quantity, potential gaps must be addressed in labeling and organization, and highlight risks of ignoring ethical considerations.
• Decisiveness is key during the training data decisions, from featurization to synthetic data use, ensuring AI models are effective, accurate, and fair.

Featurization

• Extract and structure data for model learning.

Synthetic Data

• Fill gaps with realistic synthetic datasets when needed.

Licensing Choices

• Evaluate the cost-benefit of external data sources.

Integration Strategies

• Avoid data silos by creating unified systems.

Data Alignment

• Ensure training data reflects real-world scenarios.

Caveats

• Do not over-rely on synthetic data without validation, clarify the trade-offs of licensing third-party data, address challenges in unifying disparate datasets, or highlight risks of poor-quality or biased training data.
• Reducing bias and upholding ethical standards in AI ensures fair, reliable, and user-aligned outcomes.

Diverse Datasets

• Build inclusive datasets to minimize biases.

Bias Audits

• Regularly review models for potential ethical issues.

Lessons from Failures

• Learn from missteps, such as Amazon's biased hiring tool.

Transparency in AI

• Communicate how data and models are built and used.

Ethical Culture

• Foster organizational awareness around fairness in AI.

Caveats

• All biases are not detectable , clarification needed for cross-functional reviews of AI fairness, the feasibility of eliminating bias needs to be addressed, or the importance of transparency highlighted in user-facing applications.
• Continuously monitoring model performance ensures AI solutions remain relevant, accurate, and aligned with user expectations.

Effectiveness Tracking

• Regularly evaluate model success metrics.

Learning from Failures

• Analyze examples such as Google Flu Trends to improve.

Customer-Centric Metrics

• Align performance monitoring with user priorities.

Updating Models

• Address model drift to maintain accuracy.

Transparent Metrics

• Share performance insights with stakeholders to build trust.

Caveats

• Don't be complacent with early model success , importance of ongoing monitoring and iteration must be clarified, challenges must be addressed in measuring complex customer outcomes, and risks highlighted in neglecting updates/model drift .
• Scalable data pipelines ensure AI systems can process large data volumes efficiently while supporting global operations.

Global Scalability

• Handle real-time updates at scale, such as Uber's ride-matching system.

Reliable Data Flow

• Integrate diverse sources for seamless AI recommendations, as seen with LinkedIn.

Pipeline Optimization

• Eliminate bottlenecks to speed up insights, as seen with Facebook's AI tools.

Consistency in Delivery

• Ensure pipelines adapt to growing data demands.

Collaboration with Teams

• Align data engineering and AI requirements for streamlined workflows.

Caveats

• Building pipelines without understanding future scalability needs should be avoided and the need clarified for redundancy to prevent data flow disruptions, challenges must be addressed when integrating multiple/disparate data sources, and risks highlighted in technical debt when pipelines aren't maintained.
• Effective data pipelines transform raw data into actionable insights while ensuring reliability and accuracy throughout the AI process.

Data Flow Overview

• Raw data should be extracted, transformed, and loaded into warehouses or lakes for analysis.

Transform and Stage

• Data should be prepared for AI models via cleaning and structuring.

Collaboration Needs

• Data scientists and engineers must align on pipeline requirements.

End-User Insights

• Data should be presented in a clear and actionable format for decision-making.

Scalable Design

• Pipelines must adapt to handle increasing data volume and greater complexity.

Caveats

• Avoid overly complex pipeline designs without clear goals and ensure pipelines must support both current and future use cases, address potential misalignment between data engineering and AI teams, and highlight the importance of monitoring pipeline performance.
• Ensuring data privacy and compliance safeguards customer trust while avoiding penalties and aligning to global regulations.

Regulatory Compliance

• Must adhere to laws such as GDPR, HIPAA, and local privacy standards.

Ethical Data Use

• AI models should respect user privacy and ethical considerations.

Risk Mitigation

• Regular audits to prevent misuse of customer’s data.

Transparent Practices

• Communicate how data yields trust with stakeholders.

Adapt to Changes

• Stay updated on evolving compliance standards and policies.

Caveats

• Overlooking regional data protection laws should be avoided, compliance is ongoing and proactive, potential resistance should be addressed for implementing strict data policies, and reputational risks of non-compliance must be highlighted.
• Adhering to established standards ensures data quality, fairness, and security in AI solutions, building user trust and credibility.

Data Quality Standards

• ISO/IEC 25012 ensures consistency and cleanliness.

Fairness Guidelines

• IEEE 7010 focuses on bias mitigation during decision-making.

Privacy Compliance

• GDPR and SOC 2 protect user data in global markets.

Industry-Specific Standards

• HIPAA and PCI DSS safeguard healthcare and financial data.

AI-Specific Frameworks

• AI Fairness 360 and ISO/IEC 23053 guide ethical and technical practices.

Caveats

• Do not assume all standards apply equally across industries and the importance must be clarified of selecting relevant frameworks for your use case, must address challenges when implementing multiple overlapping standards, and must highlight the need for ongoing training and compliance practices.
• Teams prioritize essential data features that align with product goals, which will balance cost, impact, and feasibility.

Activity Goal

• Decide collaboratively which data features to invest in for any AI product.

Budget Constraints

• Each team member has limited funds, encouraging tough decisions to be made.

Feature Prioritization

• Evaluate features based on impact, need, and alignment with product outcomes.

Collaborative Decision-Making

• Use Mural to discuss and finalize group choices for what to include in a product.

Strategic Focus

• Ensure purchased features address the most critical product needs.

Caveats

• Avoid prioritizing features that don't directly impact product outcomes, clarify aligning decisions with the target user's needs, address disagreements to focus on objective criteria, and highlight iterative nature of prioritizing data features over time.
• Achieving product-to-market fit requires balancing user value, business viability, and organizational feasibility before scaling is considered.

Customer Value

• Ensure a product solves real user problems.

Business Viability

• Products should align the solutions with revenue goals as well as market demands.

Organizational Feasibility

• Current resources/capabilities support any effort.

Iterative Validation

• Continuously test and refine your product to maintain fit.

Scaling Readiness

• Ensure any product can adapt because user demand is always growing.

Caveats

• Avoid scaling without validation, short-term goals need to be balanced with long-term viability, address challenges of aligning diverse stakeholder expectations, and highlight achieving fit is ongoing not a one-time milestone.
• Evaluating value, viability, and feasibility ensures AI solutions aligns with user needs and organizational goals is important throughout the whole product lifecycle.

Value Check

• Products tangible benefits to users?

Viability Assessment

• Solutions succeed financially and strategically?

Feasibility Review

• Is effort achievable with resources and skills?

Stakeholder Alignment

• Decisions-makers must buy in BEFORE you move forward.

Risk Awareness

• Identify and mitigate early risks early in the product process.

Caveats

• You can't start a good product without having core questions, importance of cross-functional input during evaluation, addressing resistance when slowing down, and highlighting how evaluation helps reduce future failiures.
• Evaluating trade-offs between risk and reward ensures high-impact solutions that justify the effort is key to proper resource allocation.

Effort-Impact Balance

• Assess the likely outcome to make sure the resource of investment make sense.

Risk Awareness

• Pinpoint the uncertainty and their potential consequences.

Customer Value

• Outcomes need to show impact to end users consistently.

Iterative Validation

• Tests should be small scale to gauge feasibility before escalating.

Long-Term Perspective

• Today decisions are the future.

Caveats

• Avoid unclear ROI at all costs, effectively measure effect and strategy effectiveness, have a means to tackle prioritization, and organization strategy must be in alignment.
• Testing ideas with lightweight experiments cuts risk while addressing custom/business requirements more efficiently prior to all-in developments.

Minimize Costly Mistakes

• Build production-quality software prematurely.

Validate Assumptions

• Identify/test riskiest variables.

Iterative Development

• Refine ideas with incremental testing.

Learn Before You Build

• Get actionable insights with prototypes.

Dual-Track Development

• Balance data with delivery to create solutions effectively.

Caveats

• Avoid high-fidelity tests when lightweight will do, dual-track development requires collaboration not competition, there is resistance to being slow to validate, and learning cheaply/quickly is important.
• Success metrics assess whether items can be successfully solved while creating usable value.

Problem Solving

• Create address to real customer issues proactively.

Meaningful Value

• Measure data of task/satisfaction of users consistently.

Customer Alignment

• Keep customer needs direct and prioritize them.

Iterative Improvement

• Features are based on metrics.

Outcome Focus

• Prioritize metrics/outcomes that deliver on requirements.

Caveats

• Use customer feedback alongside analytics, have a means to clearly determine goals based on metrics of success, have a means to collect customer feedback accurately, and ensure your metrics in line from real-world feedback sources.
• To sustain any business you must assess feasibility properly.

Viability

• Analyze revs, compare to market caps and structures.

Scalability

• Know if the solution is ready for adoption at rapid scale.

Operational Stability

• See uptime rates, efficiencies, and failure rate.

Long-Term Sustainability

• Products withstand testing thoroughly.

Alignment with Strategy

• Metrics should tie to wider goals.

Caveats

• Don't sacrifice long-term gains for shorter gains, balance needs for customers and the business, address issues of scalability, or link success to actionable analytics.
• Tracking health ensures resources stable and financially viable/secure to sustain customer requirements.

Viability Insights

• Simulation is used to price and validate business viability.

Scalability Validation

• Tests must undergo systems when resources are under stress.

Efficiency Metrics

• Integration must be ready and monitored.

Operational Resilience

• Track failure/readiness and risks.

Market Potential

• Know potential with trend analysis.

Caveats

• Must track benchmarks for success/failures, must test product consistently, there can be issues for reliability of simulations and a business strategy must have solid alignment.
• Integrating compliance in ethics from the onset will create a steady stream of trust/relationship building to support regular standards.

Privacy by Design

• Tools like Apple's can integrate compliance.

Ethics Committees

• Examples are Microsoft's Aether.

Bias Mitigation

• Fairness models and bias identifiers.

Customer Trust to Promote Fairness

• It will help the company be transparent.

Proactive Standards

• Can create a culture without non-compliance and helps the long-term success.

Caveats

• Avoid thinking this is an after-thought, prioritize ethics for better long-term relationships, and address how to penalize non-ethical behavior from a risk point.
• How do you measure if you're helping you customers resolve pain points and are their expectations being met. If adoption is high, desirability and usability are driving the satisfaction rates.

Desirability Indicators

• High adoption, interest, and engagement.

Usability Testing

• Low amount of usability.

Behavioral Insights

• Look at task completion and session durations.

Customer Feedback

• Get stories that tell users/reactions to your product(s).

Iterative Design

• Test the system and refine it.

Caveats

• Avoid using just quantitative data, clarify metrics should directly impact product design, show that continued use/engagement is happening, and that usability is important/highlight its relevance.
• Validation must ensure that a product will align with user needs while lessening waste that cause resources to be stuck.

Riskiest Assumptions

• Focus on the unknowns.

Lightweight Testing

• Cost effective.

Customer Validation

• Ensure its solving real problems.

Caveats

• Save some testing for the low tiers to help reduce the amount of stress on internal personnel.
• Match goals to the business users properly.

Avoid a Clear Hypothesis

• Make sure team is aligned.

Don't Validate Immediate Validity

• Ensure any goals are clear and that teams do not disagree.
• Must have a direct message of promise delivered, and how that will align to any target product.

Use Data to Explain

• Show how you solve customer issues.

Use Data to Show Users

• Effective, simple interactions.

Don't Over Promise Features

• Must be accurate.

Remember That Success Requires

• It is not all about viability or data alone, there is must that can align it together, and highlight what value you provide your customer.

Team Expertise and Complexity Should Match

• Success in matching means its simple for the members for project delivery.

Skill Alignment

• Match ability with the requirement.

Scalability Alignment

• Design around future.

Experimentation

• Amazon A/B testing helps accelerate insights.

Strategic Outsourcing

• Simplify for experts.

Make Sure You Support Skill Development

• Allow time to handle them.

Caveats

• Don't overburden teams, be wary of outsourcing versus using in-house, have a good plan, and ensure planning will support any gaps.
• Identifying key metrics that the product meets with scalability and usability helps drive idea success.

Desirability Metrics

• Adoption and engagement are good measures of early use. ###Usability Metrics
• Times on task.

Feasibility Validation

• The product must perform and under stress.

Viability Confirmation

Financial and costing model.

• Strategy and goals of the business are in alignment with the efforts.

Don't Overuse Metrics

• Too much focus can lead to issues and not decisions.
• Prioritize to avoid the wrong data.
• Highlight what might be needed/how it plays out during new requirements.
• Regulations and ethically sound practices ensure strategies are competitive and sound.
• Trends might shift so compliance matters.

Funding

• The tides can turn.

Ethics

• There can be many challenges to follow, be ready.
• Reduce misuse with Al tools early.
• Ensure sustainability by making sure that it meets the carbon tax.
• Be ready for proactive strategies- maintain your value in the market.
• Mitigate any risks and test data, and be ready to respond to whatever is headed your way for the business.
• Monitor any changes to what's going and make sure to adapt accordingly.
• Address problems in strategy for mitigation risks.
• Avoid single-silo mentality- collaborate to respond.
• Proactively adapt to trends with your company roadmaps.
• The team has insight and adapts through developing strategies through trends.

Regulation Developments

• There may be chip issues impacting market barriers.

Ethics

• Ethical practices should be thought of for tools proactively.
• Increase Al usage for reporting across the company with sustainability.
• Reactions trends should be reviewed and proactive analytics should define the market for you.
• Be ready to adapt and refine quickly.
• Over reacting over news articles creates issues, have different types of context, and be ready to prioritize external events.
• Categorizing the events will help you get the momentum you needed for Al initiatives.
• Needs for the business should prioritize data analysis and funding models, ethical practices, and innovation.
• Transparency from AI- be clear.
• Ensure you're evolving.
• Over planning your resources will have an impact, and prioritization will need to be adjusted from it.
• PESTel and prioritization will help manage those risks/changes in a big way.
• Frameworks are important for PESTel, and risks should be identified and adapted early on.
• Market impacts ensure forces reshape.
• Adaptation over external practices is what ensures product viability.

Caveats

• Try to involve cross-functional integration to highlight success.
• PESTel is there to help brainstorm key items, and make sure it's done in a limited fashion, there needs to evaluation with the PEST Framework for opportunities and risks.
• There has to be individual brainstorming but not for too long .
• Prioritize by knowing what you are going to monitor with the team and collaborate for the factors and insights for future.
• There has to be consensus across the team, and documentation insight for any upcoming updates.
• Understand what applies from homework, and build key skills by knowing tools for any scenario.
• Have clear expectations from deliverables for the exercise(s) to learn concepts.
• Minimizing risk and learning helps with the bets that are treating decisions which has a meaningful approach.
• Clear outcomes will need to focus on the selected goals and bets.

Caveats

• Don't be afraid to ask when you're uncertain, and focus on learning to reflect from all mistakes made.
• High failure rates means avoiding wasted resources through validation effectively.
• Make sure you cost those efforts, and minimize them through iteration.
• With ~60% failure rates, 20% damage the business and the solution should be validated before it's ready- you must then cost optimize and iterate properly to limit any discouragement of the business innovation.
• Try to get quick testable bets on the testable items, because solutions will fail.
• Focus on testing the assumptions and the rates too early and too often.
• The impact, and best practices should address any concerns and highlight any concerns for cutting costs/failures early on.
• Limit high fidelity for prototypes to minimize issues, where risks can come into play.
• Smaller bets have quicker iters allowing low risk, the more resources you add the higher the value. As such, you could run into bigger issues.
• A mix of big/small helps to keep that even balance.
• Never put all/remove all in one basket and highlight investment requirements as a product line.
• Never get overhyped with market products without validating what will come of the demand.
• Complexity and estimates overhype results.
• With no revenue, there may be execution issues/regulatory costs/adoption issues.
• Ensure you have clear guidelines and timelines in place to avoid issues.
• External trends that can disrupt/reshape business.
• Be prepared to know the right changes that need to be handled early on.
• Adaptation and planning are key. "Understand Al challenges by addressing and working well alongside external factors.
• Trends, regulations, and market direction can sway progress, and having transparency/data is key.
• What metrics should you use? Use 146 to help prioritize responses/metrics, so you can adapt more effectively.
• Be proactive by planning, and ensure your company integrates an honest/clear vision for customer needs.